Our proposed restricting methodology first identifies whether a proposer or a validator is benign or malicious. There are a number of quantified user assessment techniques such as opinion-based or applying ML algorithms on previous blockchain transaction data described in section . Our focus is to use the quantified values in a way so as to prevent illicit activities' transactions from getting added into the blockchain. Our methodology is based on a set of time-variant reputation values computed by each node, based on an agreed-upon ML model for computing the same. The reputation index is also termed as reputation score list, given as \(sL_{x} = [p^{x,1} p^{x,2}, \cdots , p^{x,|N|}]\)  stored by \(n_{x} \in N\) . Here \(p^{x,y} \in [0,1]\)  is the reputation value assigned by \(n_{x}\)  to \(n_{y}\) , while \(n_{x}, n_{y} \in N\) . As an example of computation methodology for such values, in [1]}, the authors propose time-variant reputation scores (related to a user being malicious/benign). Here, their approach extract features from the transactions done by each user for each day and clusters the users. If a user shows behavioral similarity with known malicious users, the user gets tagged as a suspect. Over time the proposed model computes the probability of being malicious for the considered duration. The outcome of the model is same as the reputation score for our case. Note that lower the value of \(p^{j,i}\) , the more suspicious/malicious \(n_{i}\)  is as perceived by \(n_{j}\) . In our methodology, every user in the network is equipped with its own module to compute such reputation scores for all the users. Our method does not put any additional burden to the blockchain network communication because the input for the model is the transactions that are already replicated at each node. Such computation can be done separately from the blockchain consensus process.
